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How Risk Changes the Way Economists See Inflation and Wagesby@keynesian

How Risk Changes the Way Economists See Inflation and Wages

by Keynesian TechnologyDecember 8th, 2024
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This section outlines the derivation of the Phillips curve, log-linearized around stochastic equilibrium. Unlike traditional approximations, the coefficients in this framework capture higher moments of distributions, offering new insights into dynamics under idiosyncratic or large aggregate risks. The approach leverages stochastic Grobman-Hartman theorems and introduces computational techniques to address structural errors, paving the way for advanced analysis of dynamic systems.
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Author:

(1) David Staines.

Abstract

1 Introduction

2 Mathematical Arguments

3 Outline and Preview

4 Calvo Framework and 4.1 Household’s Problem

4.2 Preferences

4.3 Household Equilibrium Conditions

4.4 Price-Setting Problem

4.5 Nominal Equilibrium Conditions

4.6 Real Equilibrium Conditions and 4.7 Shocks

4.8 Recursive Equilibrium

5 Existing Solutions

5.1 Singular Phillips Curve

5.2 Persistence and Policy Puzzles

5.3 Two Comparison Models

5.4 Lucas Critique

6 Stochastic Equilibrium and 6.1 Ergodic Theory and Random Dynamical Systems

6.2 Equilibrium Construction

6.3 Literature Comparison

6.4 Equilibrium Analysis

7 General Linearized Phillips Curve

7.1 Slope Coefficients

7.2 Error Coefficients

8 Existence Results and 8.1 Main Results

8.2 Key Proofs

8.3 Discussion

9 Bifurcation Analysis

9.1 Analytic Aspects

9.2 Algebraic Aspects (I) Singularities and Covers

9.3 Algebraic Aspects (II) Homology

9.4 Algebraic Aspects (III) Schemes

9.5 Wider Economic Interpretations

10 Econometric and Theoretical Implications and 10.1 Identification and Trade-offs

10.2 Econometric Duality

10.3 Coefficient Properties

10.4 Microeconomic Interpretation

11 Policy Rule

12 Conclusions and References


Appendices

A Proof of Theorem 2 and A.1 Proof of Part (i)

A.2 Behaviour of ∆

A.3 Proof Part (iii)

B Proofs from Section 4 and B.1 Individual Product Demand (4.2)

B.2 Flexible Price Equilibrium and ZINSS (4.4)

B.3 Price Dispersion (4.5)

B.4 Cost Minimization (4.6) and (10.4)

B.5 Consolidation (4.8)

C Proofs from Section 5, and C.1 Puzzles, Policy and Persistence

C.2 Extending No Persistence

D Stochastic Equilibrium and D.1 Non-Stochastic Equilibrium

D.2 Profits and Long-Run Growth

E Slopes and Eigenvalues and E.1 Slope Coefficients

E.2 Linearized DSGE Solution

E.3 Eigenvalue Conditions

E.4 Rouche’s Theorem Conditions

F Abstract Algebra and F.1 Homology Groups

F.2 Basic Categories

F.3 De Rham Cohomology

F.4 Marginal Costs and Inflation

G Further Keynesian Models and G.1 Taylor Pricing

G.2 Calvo Wage Phillips Curve

G.3 Unconventional Policy Settings

H Empirical Robustness and H.1 Parameter Selection

H.2 Phillips Curve

I Additional Evidence and I.1 Other Structural Parameters

I.2 Lucas Critique

I.3 Trend Inflation Volatility

7 General Linearized Phillips Curve

This section sketches the derivation of the Phillips curve log-linearized around any stochastic equilibrium. It rests on an application of two stochastic Grobman-Hartman theorems. Its formal justification will be supplied in the next two sections. The first part focuses on the slope coefficients and is supported by Appendix E, whilst the second solves out the structural errors around ZINSS. A full analysis of the properties of the error coefficients elsewhere is beyond the scope of this paper.


The surprising feature of this new linear approximation is that although it is certainty equivalent in deviations, this is not true of the whole system because the coefficients represent higher moments of the distributions. This novel aspect will allow this approximation to summarize critical aspects of the dynamical system. This approximation framework should prove a natural setting to analyze the dynamic and statistical properties models with idiosyncratic or large aggregate risks. This will require new computational and econometric routines, which I will touch on here.


The main quantitative work will focus on limiting cases comparable or in some cases equivalent to existing linearization designs. The solution will feature extensively manipulations with the lag operator. These will reappear prominently in the bifurcation analysis of Section 9, where I will also draw precise connections between terms in the Phillips curve and singularities like those discussed in Section 3.



This paper is available on arxiv under CC 4.0 license.